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<div class="title">covariance_sampling.hpp</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Software License Agreement (BSD License)</span></div>
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<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> * Point Cloud Library (PCL) - www.pointclouds.org</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> * Copyright (c) 2009-2012, Willow Garage, Inc.</span></div>
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<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="preprocessor">#ifndef PCL_FILTERS_IMPL_COVARIANCE_SAMPLING_H_</span></div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="preprocessor">#define PCL_FILTERS_IMPL_COVARIANCE_SAMPLING_H_</span></div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160; </div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<span class="preprocessor">#include &lt;pcl/common/eigen.h&gt;</span></div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="preprocessor">#include &lt;pcl/filters/covariance_sampling.h&gt;</span></div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="preprocessor">#include &lt;list&gt;</span></div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160; </div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>NT&gt; <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;<a class="code" href="classpcl_1_1_covariance_sampling.html">pcl::CovarianceSampling&lt;PointT, PointNT&gt;::initCompute</a> ()</div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;{</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;  <span class="keywordflow">if</span> (!FilterIndices&lt;PointT&gt;::initCompute ())</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;    <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160; </div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;  <span class="keywordflow">if</span> (num_samples_ &gt; indices_-&gt;size ())</div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;  {</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;[pcl::CovarianceSampling::initCompute] The number of samples you asked for (%d) is larger than the number of input indices (%lu)\n&quot;</span>,</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;               num_samples_, indices_-&gt;size ());</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;    <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;  }</div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160; </div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;  <span class="comment">// Prepare the point cloud by centering at the origin and then scaling the points such that the average distance from</span></div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;  <span class="comment">// the origin is 1.0 =&gt; rotations and translations will have the same magnitude</span></div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;  Eigen::Vector3f centroid (0.f, 0.f, 0.f);</div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> p_i = 0; p_i &lt; indices_-&gt;size (); ++p_i)</div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;    centroid += (*input_)[(*indices_)[p_i]].getVector3fMap ();</div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;  centroid /= float (indices_-&gt;size ());</div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160; </div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;  scaled_points_.resize (indices_-&gt;size ());</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;  <span class="keywordtype">double</span> average_norm = 0.0;</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> p_i = 0; p_i &lt; indices_-&gt;size (); ++p_i)</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;  {</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;    scaled_points_[p_i] = (*input_)[(*indices_)[p_i]].getVector3fMap () - centroid;</div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;    average_norm += scaled_points_[p_i].norm ();</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;  }</div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;  average_norm /= double (scaled_points_.size ());</div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> p_i = 0; p_i &lt; scaled_points_.size (); ++p_i)</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;    scaled_points_[p_i] /= <span class="keywordtype">float</span> (average_norm);</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160; </div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;  <span class="keywordflow">return</span> (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;}</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160; </div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>NT&gt; <span class="keywordtype">double</span></div>
<div class="line"><a name="l00085"></a><span class="lineno"><a class="line" href="classpcl_1_1_covariance_sampling.html#a82eac9e85ffed525bb467e3cd58a87e4">   85</a></span>&#160;<a class="code" href="classpcl_1_1_covariance_sampling.html#a82eac9e85ffed525bb467e3cd58a87e4">pcl::CovarianceSampling&lt;PointT, PointNT&gt;::computeConditionNumber</a> ()</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;{</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;  Eigen::Matrix&lt;double, 6, 6&gt; covariance_matrix;</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;  <span class="keywordflow">if</span> (!<a class="code" href="group__common.html#gac36b146ec26b1ceb7be43a9ecaa010c4">computeCovarianceMatrix</a> (covariance_matrix))</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    <span class="keywordflow">return</span> (-1.);</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160; </div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;  Eigen::EigenSolver&lt;Eigen::Matrix&lt;double, 6, 6&gt; &gt; eigen_solver;</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;  eigen_solver.compute (covariance_matrix, <span class="keyword">true</span>);</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160; </div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;  Eigen::MatrixXcd complex_eigenvalues = eigen_solver.eigenvalues ();</div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160; </div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;  <span class="keywordtype">double</span> max_ev = -std::numeric_limits&lt;double&gt;::max (),</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;      min_ev = std::numeric_limits&lt;double&gt;::max ();</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; 6; ++i)</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;  {</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    <span class="keywordflow">if</span> (real (complex_eigenvalues (i, 0)) &gt; max_ev)</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;      max_ev = real (complex_eigenvalues (i, 0));</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160; </div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;    <span class="keywordflow">if</span> (real (complex_eigenvalues (i, 0)) &lt; min_ev)</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;      min_ev = real (complex_eigenvalues (i, 0));</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;  }</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160; </div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;  <span class="keywordflow">return</span> (max_ev / min_ev);</div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;}</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160; </div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160; </div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>NT&gt; <span class="keywordtype">double</span></div>
<div class="line"><a name="l00113"></a><span class="lineno"><a class="line" href="classpcl_1_1_covariance_sampling.html#a8f2b37a9be66d9745201d5cb1fa36774">  113</a></span>&#160;<a class="code" href="classpcl_1_1_covariance_sampling.html#a82eac9e85ffed525bb467e3cd58a87e4">pcl::CovarianceSampling&lt;PointT, PointNT&gt;::computeConditionNumber</a> (<span class="keyword">const</span> Eigen::Matrix&lt;double, 6, 6&gt; &amp;covariance_matrix)</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;{</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;  Eigen::EigenSolver&lt;Eigen::Matrix&lt;double, 6, 6&gt; &gt; eigen_solver;</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;  eigen_solver.compute (covariance_matrix, <span class="keyword">true</span>);</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160; </div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;  Eigen::MatrixXcd complex_eigenvalues = eigen_solver.eigenvalues ();</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160; </div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;  <span class="keywordtype">double</span> max_ev = -std::numeric_limits&lt;double&gt;::max (),</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;      min_ev = std::numeric_limits&lt;double&gt;::max ();</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; 6; ++i)</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;  {</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    <span class="keywordflow">if</span> (real (complex_eigenvalues (i, 0)) &gt; max_ev)</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;      max_ev = real (complex_eigenvalues (i, 0));</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160; </div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    <span class="keywordflow">if</span> (real (complex_eigenvalues (i, 0)) &lt; min_ev)</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;      min_ev = real (complex_eigenvalues (i, 0));</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;  }</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160; </div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;  <span class="keywordflow">return</span> (max_ev / min_ev);</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;}</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160; </div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160; </div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>NT&gt; <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00137"></a><span class="lineno"><a class="line" href="classpcl_1_1_covariance_sampling.html#ab2210a7ae6ce3f6bf71e13ee3cf9249b">  137</a></span>&#160;<a class="code" href="classpcl_1_1_covariance_sampling.html#ab2210a7ae6ce3f6bf71e13ee3cf9249b">pcl::CovarianceSampling&lt;PointT, PointNT&gt;::computeCovarianceMatrix</a> (Eigen::Matrix&lt;double, 6, 6&gt; &amp;covariance_matrix)</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;{</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;  <span class="keywordflow">if</span> (!initCompute ())</div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;    <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160; </div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;  <span class="comment">//--- Part A from the paper</span></div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;  <span class="comment">// Set up matrix F</span></div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;  Eigen::Matrix&lt;double, 6, Eigen::Dynamic&gt; f_mat = Eigen::Matrix&lt;double, 6, Eigen::Dynamic&gt; (6, indices_-&gt;size ());</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> p_i = 0; p_i &lt; scaled_points_.size (); ++p_i)</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;  {</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    f_mat.block&lt;3, 1&gt; (0, p_i) = scaled_points_[p_i].cross (</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;                                     (*input_normals_)[(*indices_)[p_i]].getNormalVector3fMap ()).<span class="keyword">template</span> cast&lt;double&gt; ();</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;    f_mat.block&lt;3, 1&gt; (3, p_i) = (*input_normals_)[(*indices_)[p_i]].getNormalVector3fMap ().template cast&lt;double&gt; ();</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;  }</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160; </div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;  <span class="comment">// Compute the covariance matrix C and its 6 eigenvectors (initially complex, move them to a double matrix)</span></div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;  covariance_matrix = f_mat * f_mat.transpose ();</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;}</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160; </div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>NT&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00159"></a><span class="lineno"><a class="line" href="classpcl_1_1_covariance_sampling.html#a2731ee72e45ed3e80f0580cd2eab9096">  159</a></span>&#160;<a class="code" href="classpcl_1_1_covariance_sampling.html#a7d073302aaf53592ebaad290041162c4">pcl::CovarianceSampling&lt;PointT, PointNT&gt;::applyFilter</a> (std::vector&lt;int&gt; &amp;sampled_indices)</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;{</div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;  <span class="keywordflow">if</span> (!initCompute ())</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160; </div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;  <span class="comment">//--- Part A from the paper</span></div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;  <span class="comment">// Set up matrix F</span></div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;  Eigen::Matrix&lt;double, 6, Eigen::Dynamic&gt; f_mat = Eigen::Matrix&lt;double, 6, Eigen::Dynamic&gt; (6, indices_-&gt;size ());</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> p_i = 0; p_i &lt; scaled_points_.size (); ++p_i)</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;  {</div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;    f_mat.block&lt;3, 1&gt; (0, p_i) = scaled_points_[p_i].cross (</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;                                     (*input_normals_)[(*indices_)[p_i]].getNormalVector3fMap ()).<span class="keyword">template</span> cast&lt;double&gt; ();</div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;    f_mat.block&lt;3, 1&gt; (3, p_i) = (*input_normals_)[(*indices_)[p_i]].getNormalVector3fMap ().template cast&lt;double&gt; ();</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;  }</div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160; </div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;  <span class="comment">// Compute the covariance matrix C and its 6 eigenvectors (initially complex, move them to a double matrix)</span></div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;  Eigen::Matrix&lt;double, 6, 6&gt; c_mat (f_mat * f_mat.transpose ());</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160; </div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;  Eigen::EigenSolver&lt;Eigen::Matrix&lt;double, 6, 6&gt; &gt; eigen_solver;</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;  eigen_solver.compute (c_mat, <span class="keyword">true</span>);</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;  Eigen::MatrixXcd complex_eigenvectors = eigen_solver.eigenvectors ();</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160; </div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;  Eigen::Matrix&lt;double, 6, 6&gt; x;</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; 6; ++i)</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; 6; ++j)</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;      x (i, j) = real (complex_eigenvectors (i, j));</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160; </div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160; </div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;  <span class="comment">//--- Part B from the paper</span></div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;<span class="comment"></span>  std::vector&lt;size_t&gt; candidate_indices;</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;  candidate_indices.resize (indices_-&gt;size ());</div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> p_i = 0; p_i &lt; candidate_indices.size (); ++p_i)</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;    candidate_indices[p_i] = p_i;</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160; </div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;  <span class="comment">// Compute the v 6-vectors</span></div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;  <span class="keyword">typedef</span> Eigen::Matrix&lt;double, 6, 1&gt; Vector6d;</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;  std::vector&lt;Vector6d, Eigen::aligned_allocator&lt;Vector6d&gt; &gt; v;</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;  v.resize (candidate_indices.size ());</div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> p_i = 0; p_i &lt; candidate_indices.size (); ++p_i)</div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;  {</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;    v[p_i].block&lt;3, 1&gt; (0, 0) = scaled_points_[p_i].cross (</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;                                  (*input_normals_)[(*indices_)[candidate_indices[p_i]]].getNormalVector3fMap ()).<span class="keyword">template</span> cast&lt;double&gt; ();</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;    v[p_i].block&lt;3, 1&gt; (3, 0) = (*input_normals_)[(*indices_)[candidate_indices[p_i]]].getNormalVector3fMap ().template cast&lt;double&gt; ();</div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;  }</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160; </div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160; </div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;  <span class="comment">// Set up the lists to be sorted</span></div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;  std::vector&lt;std::list&lt;std::pair&lt;int, double&gt; &gt; &gt; L;</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;  L.resize (6);</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160; </div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; 6; ++i)</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;  {</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> p_i = 0; p_i &lt; candidate_indices.size (); ++p_i)</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;      L[i].push_back (std::make_pair (p_i, fabs (v[p_i].dot (x.block&lt;6, 1&gt; (0, i)))));</div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160; </div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;    <span class="comment">// Sort in decreasing order</span></div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;    L[i].sort (sort_dot_list_function);</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;  }</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160; </div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;  <span class="comment">// Initialize the 6 t&#39;s</span></div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;  std::vector&lt;double&gt; t (6, 0.0);</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160; </div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;  sampled_indices.resize (num_samples_);</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;  std::vector&lt;bool&gt; point_sampled (candidate_indices.size (), <span class="keyword">false</span>);</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;  <span class="comment">// Now select the actual points</span></div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> sample_i = 0; sample_i &lt; num_samples_; ++sample_i)</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;  {</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;    <span class="comment">// Find the most unconstrained dimension, i.e., the minimum t</span></div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;    <span class="keywordtype">size_t</span> min_t_i = 0;</div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; 6; ++i)</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;    {</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;      <span class="keywordflow">if</span> (t[min_t_i] &gt; t[i])</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;        min_t_i = i;</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;    }</div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160; </div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;    <span class="comment">// Add the point from the top of the list corresponding to the dimension to the set of samples</span></div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;    <span class="keywordflow">while</span> (point_sampled [L[min_t_i].front ().first])</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;      L[min_t_i].pop_front ();</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160; </div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;    sampled_indices[sample_i] = L[min_t_i].front ().first;</div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;    point_sampled[L[min_t_i].front ().first] = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;    L[min_t_i].pop_front ();</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160; </div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;    <span class="comment">// Update the running totals</span></div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; 6; ++i)</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;    {</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;      <span class="keywordtype">double</span> val = v[sampled_indices[sample_i]].dot (x.block&lt;6, 1&gt; (0, i));</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;      t[i] += val * val;</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;    }</div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;  }</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160; </div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;  <span class="comment">// Remap the sampled_indices to the input_ cloud</span></div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; sampled_indices.size (); ++i)</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;    sampled_indices[i] = (*indices_)[candidate_indices[sampled_indices[i]]];</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;}</div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160; </div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160; </div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>NT&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00259"></a><span class="lineno"><a class="line" href="classpcl_1_1_covariance_sampling.html#a7d073302aaf53592ebaad290041162c4">  259</a></span>&#160;<a class="code" href="classpcl_1_1_covariance_sampling.html#a7d073302aaf53592ebaad290041162c4">pcl::CovarianceSampling&lt;PointT, PointNT&gt;::applyFilter</a> (<a class="code" href="classpcl_1_1_point_cloud.html">Cloud</a> &amp;output)</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;{</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;  std::vector&lt;int&gt; sampled_indices;</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;  applyFilter (sampled_indices);</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160; </div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;  output.<a class="code" href="classpcl_1_1_point_cloud.html#a2d60b6927b31ef89cd3b97e8173ea4aa">resize</a> (sampled_indices.size ());</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;  output.<a class="code" href="classpcl_1_1_point_cloud.html#a82e0be055a617e5e74102ed62712b352">header</a> = input_-&gt;header;</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;  output.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 1;</div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;  output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = uint32_t (output.size ());</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;  output.<a class="code" href="classpcl_1_1_point_cloud.html#a3ca88d8ebf6f4f35acbc31cdfb38aa94">is_dense</a> = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; sampled_indices.size (); ++i)</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;    output[i] = (*input_)[sampled_indices[i]];</div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;}</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160; </div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160; </div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;<span class="preprocessor">#define PCL_INSTANTIATE_CovarianceSampling(T,NT) template class PCL_EXPORTS pcl::CovarianceSampling&lt;T,NT&gt;;</span></div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160; </div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;<span class="preprocessor">#endif </span><span class="comment">/* PCL_FILTERS_IMPL_COVARIANCE_SAMPLING_H_ */</span><span class="preprocessor"></span></div>
<div class="ttc" id="aclasspcl_1_1_covariance_sampling_html"><div class="ttname"><a href="classpcl_1_1_covariance_sampling.html">pcl::CovarianceSampling</a></div><div class="ttdoc">Point Cloud sampling based on the 6D covariances. It selects the points such that the resulting cloud...</div><div class="ttdef"><b>Definition:</b> covariance_sampling.h:63</div></div>
<div class="ttc" id="aclasspcl_1_1_covariance_sampling_html_a7d073302aaf53592ebaad290041162c4"><div class="ttname"><a href="classpcl_1_1_covariance_sampling.html#a7d073302aaf53592ebaad290041162c4">pcl::CovarianceSampling::applyFilter</a></div><div class="ttdeci">void applyFilter(Cloud &amp;output)</div><div class="ttdoc">Sample of point indices into a separate PointCloud</div><div class="ttdef"><b>Definition:</b> covariance_sampling.hpp:259</div></div>
<div class="ttc" id="aclasspcl_1_1_covariance_sampling_html_a82eac9e85ffed525bb467e3cd58a87e4"><div class="ttname"><a href="classpcl_1_1_covariance_sampling.html#a82eac9e85ffed525bb467e3cd58a87e4">pcl::CovarianceSampling::computeConditionNumber</a></div><div class="ttdeci">double computeConditionNumber()</div><div class="ttdoc">Compute the condition number of the input point cloud. The condition number is the ratio between the ...</div><div class="ttdef"><b>Definition:</b> covariance_sampling.hpp:85</div></div>
<div class="ttc" id="aclasspcl_1_1_covariance_sampling_html_ab2210a7ae6ce3f6bf71e13ee3cf9249b"><div class="ttname"><a href="classpcl_1_1_covariance_sampling.html#ab2210a7ae6ce3f6bf71e13ee3cf9249b">pcl::CovarianceSampling::computeCovarianceMatrix</a></div><div class="ttdeci">bool computeCovarianceMatrix(Eigen::Matrix&lt; double, 6, 6 &gt; &amp;covariance_matrix)</div><div class="ttdoc">Computes the covariance matrix of the input cloud.</div><div class="ttdef"><b>Definition:</b> covariance_sampling.hpp:137</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html"><div class="ttname"><a href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a></div><div class="ttdoc">PointCloud represents the base class in PCL for storing collections of 3D points.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:173</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a2185a6453f8ad905d7bdf7b45754a160"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">pcl::PointCloud::width</a></div><div class="ttdeci">uint32_t width</div><div class="ttdoc">The point cloud width (if organized as an image-structure).</div><div class="ttdef"><b>Definition:</b> point_cloud.h:413</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a2d60b6927b31ef89cd3b97e8173ea4aa"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a2d60b6927b31ef89cd3b97e8173ea4aa">pcl::PointCloud::resize</a></div><div class="ttdeci">void resize(size_t n)</div><div class="ttdoc">Resize the cloud</div><div class="ttdef"><b>Definition:</b> point_cloud.h:455</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a3ca88d8ebf6f4f35acbc31cdfb38aa94"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a3ca88d8ebf6f4f35acbc31cdfb38aa94">pcl::PointCloud::is_dense</a></div><div class="ttdeci">bool is_dense</div><div class="ttdoc">True if no points are invalid (e.g., have NaN or Inf values).</div><div class="ttdef"><b>Definition:</b> point_cloud.h:418</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a4f34b45220c57f96607513ffad0d9582"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">pcl::PointCloud::height</a></div><div class="ttdeci">uint32_t height</div><div class="ttdoc">The point cloud height (if organized as an image-structure).</div><div class="ttdef"><b>Definition:</b> point_cloud.h:415</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a82e0be055a617e5e74102ed62712b352"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a82e0be055a617e5e74102ed62712b352">pcl::PointCloud::header</a></div><div class="ttdeci">pcl::PCLHeader header</div><div class="ttdoc">The point cloud header. It contains information about the acquisition time.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:407</div></div>
<div class="ttc" id="agroup__common_html_gac36b146ec26b1ceb7be43a9ecaa010c4"><div class="ttname"><a href="group__common.html#gac36b146ec26b1ceb7be43a9ecaa010c4">pcl::computeCovarianceMatrix</a></div><div class="ttdeci">unsigned int computeCovarianceMatrix(const pcl::PointCloud&lt; PointT &gt; &amp;cloud, const Eigen::Matrix&lt; Scalar, 4, 1 &gt; &amp;centroid, Eigen::Matrix&lt; Scalar, 3, 3 &gt; &amp;covariance_matrix)</div><div class="ttdoc">Compute the 3x3 covariance matrix of a given set of points. The result is returned as a Eigen::Matrix...</div></div>
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